CMPSCI 514 : Algorithms for Data Science

Instructor: Barna Saha
Office: CS 336. Office phone: (413) 577-2510. E-mail:
Instructor Office Hour: Thu 12:45-1:45pm in CS336

Teaching Assistant: David Tench
Office Hour: Wed 3-4pm in CS207

Teaching Assistant: Raghavendra Addanki
Office Hour: Mon 4-5pm in CS207

Class Time: TuThu 11:30-12:45 pm in LederT 123
Piazza Link: We will use Piazza for all class related discussions. Sign up here.

Course Overview:

Big Data brings us to interesting times and promises to revolutionize our society from business to government, from healthcare to academia. As we walk through this digitized age of exploded data, there is an increasing demand to develop unified toolkits for data processing and analysis. In this course our main goal is to rigorously study the mathematical foundation of big data processing, develop algorithms and learn how to analyze them. Specific Topics to be covered include (subject to change):
  1. Clustering
  2. Estimating Statistical Properties of Data
  3. Near Neighbor Search
  4. Algorithms over Massive Graphs and Social Networks
  5. Learning Algorithms
  6. Randomized Algorithms

Course Details:

Text Book: We will use reference materials from the following books. Both can be downloaded for free.
Prerequisities: CMPSCI 311 and CMPSCI 240 or equivalent courses are required with grade of B or better in both the courses. All Students require proper background in algorithm design and basic probability, and will not be admitted in the course without satisfying the prerequisities.

  • Homeworks(3~4) - 30%
    -- Will consist of mathematical problems and/or programming assignments. To be done in a group of 2 to 4.
  • Mini-Exercises(3~4) - 20%
    -- Few simple exercises to be done individually.
  • Midterm - 20%
  • Endterm - 30%

Submission: All submissions must be done on moodle. For homeworks, only a single member in the group should upload a scanned handwritten document or a typed document. Please ensure that the handwriting is legible.

Late Homework Policy: No late submission is allowed unless there are compelling reasons and pre-approved by the instructor.

Previous Offering: Fall 2017